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检索条件"任意字段=7th International and Interdisciplinary Conference on Modeling and Using Context"

共 1257 条记录，以下是21-30 订阅

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Application of Artificial Intelligence Algorithms in Mathematical modeling Optimization Research 7th

Application of Artificial Intelligence Algorithms in Mathema...

引用

7th international conference on Innovative Computing, IC 2024

作者： Zeng, Qiao School of Science and Technology University of Sanya Sanya572022 China Guoliang Team Innovation Center Academician Chen UniversityofSanya Sanya572022 China

ISBN: (纸本)9789819741243

Mathematics plays an increasingly important role in students’ learning and life, especially in mathematical modeling competitions, which have become an important indicator for evaluating students’ extracurricular practical and hands-on abilities. Mathematical modeling's fundamental concept is to streamline and address real-world issues by employing mathematical representations. Its primary objective is to assist individuals in tackling seemingly insurmountable challenges encountered in their everyday experiences, find a solution to the problem by establishing a model. In the current era, China's technological development is rapidly changing, and the technical tools used for mathematical modeling have also become diverse. In particular, the integration of artificial intelligence algorithms has significantly propelled the advancement of mathematical modeling. the author's primary emphasis is on investigating how artificial neural network techniques and genetic algorithms can be applied within the context of Chinese mathematical modeling technology, in order to promote the continuous improvement of mathematical modeling level. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Genetic algorithms

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modeling and Simulation of SEPIC Converter with ANFIS Controller for DC-to-DC Power Conversion 7

Modeling and Simulation of SEPIC Converter with ANFIS Contro...

引用

7th international conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Mudigondla, Rosaiah Chenchireddy, Kalagotla Sidhu, Cheruku Jithender Patel, K. Sunil Reddy, A. Vamshi, D. Teegala Krishna Reddy Engineering College Dept. of Electrical and Electronics Engineering Telangana Hyderabad India

ISBN: (纸本)9798350340600

DC-DC power converters will play a crucial role in efficiently regulating voltage levels for various loads across different applications. Among the diverse types of DC-DC converters, the Single Ended Primary-Inductor Converter (SEPIC) has gained significant popularity due to its ability to step up or step down input voltage while offering galvanic isolation between the input and output. In this context, a novel approach to achieve precise voltage regulation and robust control under diverse operating conditions of integration, an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller with the SEPIC converter is proposed. the outcomes of this research study have a substantial impact on advancing the control strategies for DC-DC power conversion systems, offering valuable insights into the capabilities and real-time implementation potential of the ANFIS controller. © 2023 IEEE.

关键词： DC-DC converters

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A Lightweight Leader Election Algorithm for IoT: Cloud Storage Use Case 7

A Lightweight Leader Election Algorithm for IoT: Cloud Stora...

引用

7th international conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Bounceur, Ahcene Kfupm University Information And Computer Science Department Interdisciplinary Research Center Intelligent Secure Systems Saudi Arabia

ISBN: (纸本)9798400709036

In a distributed environment where IoT devices collaborate on shared data, ensuring a secure turn-taking process during data updates remains crucial. Unlike in centralized systems, the concept of mutexes is no longer applicable in distributed systems. this is where distributed locks and leader election mechanisms become essential. the BROGO (Branch Optima to Global Optimum) Leader Election Algorithm, known for its low power consumption, presents a compelling solution tailored for IoT systems. Notably, this algorithm achieves a remarkable 95% reduction in energy consumption compared to conventional techniques. In the context of this paper, we use the BROGO algorithm as a case study within the realm of cloud storage. © 2023 ACM.

关键词： Energy utilization

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Clothing Parsing Based on context Prior and Flow Alignment Pyramid 7

Clothing Parsing Based on Context Prior and Flow Alignment P...

引用

7th international conference on Cloud Computing and Big Data Analytics, ICCCBDA 2022

作者： Fan, Jingyi Wang, Shaoyu Ma, Xiao Xu, An Ye, Shaoping Shi, Xiujin Donghua University School of Computer Science and Technology Shanghai China

ISBN: (纸本)9781665487115

Clothing parsing plays an important role in the fashion industry, and it is also a significant branch of semantic segmentation. the advanced related work explores various methods to efficiently aggregate contextual information, but they do not clearly distinguish the difference of different contextual relationships. In this paper, we propose a novel network based on context Prior and Flow Alignment Pyramid for clothing parsing. Specifically, we effectively establish an original context map to exploit intra-class and inter-class contextual information. We devise a new loss function to supervise the learning of the original context map mentioned above by using a standard context map. Considering the sizes of different classes have a large degree of variation in clothing images, we fuse semantic information from multiple levels of the network to avoid small-sized items from disappearing during network propagation. To circumvent the offset that occurs in fusion process, the Flow Alignment Module (FAM) is designed to make the output contains more spatial detail information. Experiments show that our method achieves promising performance on the CFPD dataset (51.71% of mIoU and 93.23% of PA) compared with other state-of-the-Art methods. © 2022 IEEE.

关键词： Industries Cloud computing Fuses Computational modeling Clothing Semantics Big Data

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Local-Global-Aware Convolutional Transformer for Hyperspectral Image Classification 23

Local-Global-Aware Convolutional Transformer for Hyperspectr...

引用

23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wang, Yifan Min, Zhichao Jia, Sen College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781665494571

In recent years, more and more end-to-end deep learning methods have been applied to remote sensing image classification. Convolutional neural networks (CNN) is the most representative one. However, the convolution kernel that makes CNN successful also limits its ability of global perception due to its fixed size. Transformer, as an emerging model, has achieved great success in both computer vision and natural language processing (NLP). Without any convolution module, Transformer is almost entirely composed of Attention mechanism that brings it context-aware ability and makes computational parallelization easier. therefore, this paper proposes a two-branch convolutional Transformer network, which combines the local feature extraction capabilities of CNN and context-aware capabilities of Transformer to get a better feature representation while modeling dual-path long-range dependence. In addition, we flexibly combine 1D convolutional with linear projection to realize feature embedding while fusing convolutional features. the comparative experiments results on the public dataset confirm the effectiveness of our method, and the ablation experiment proves the necessity of our module and network design. © 2021 IEEE.

关键词： Adaptation models Convolution Smart cities Computational modeling Transformers Feature extraction Natural language processing

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Identifying Insecure Network Configurations through Attack modeling and Explainable AI 20th

Identifying Insecure Network Configurations Through Attack M...

引用

20th international conference on Information Systems Security

作者： thomas, Blessy thampi, Sabu M. Mukherjee, Preetam Digital Univ Kerala Thiruvananthapuram Kerala India

ISBN: (纸本)9783031800191;9783031800207

Every day, a multitude of IoT devices connect to the internet, enhancing functionality and user experience. However, this increased connectivity exposes these devices to external threats. Securing the network requires effective modeling of potential attack scenarios. the dynamic nature of IoT networks often alters these scenarios, making attack modeling challenging. In this context, identifying inappropriate network configurations that lead to insecure conditions becomes a practical alternative. Avoiding such configurations helps protect the infrastructure from threat actors. In this paper, an Explainable AI (XAI) approach using the Local Interpretable Model-Agnostic Explanations (LIME) algorithm is employed to assess the impact of various network configurations on security. the framework's effectiveness is demonstrated through a realistic IoT network example. the experiment explains how network characteristics influence insecurity, offering valuable insights into potential vulnerabilities.

关键词： IoT Network Attack modeling Explainable AI Machine Learning

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Stateful Functional modeling with Refinement (a Lean4 Framework) 19th

Stateful Functional Modeling with Refinement (a Lean4 Framew...

引用

19th international conference on Integrated Formal Methods

作者： Peschanski, Frederic Sorbonne Univ LIP6 CNRS UMR 7606 Paris France

ISBN: (纸本)9783031765537;9783031765544

We present a Lean4 framework for the formal modeling of stateful entities in the context of functional domain modeling. the main objective is to support a step-wise refinement methodology inspired by the Event-B formal method. the implementation provides the main Event-B constructions such as contexts, machines, events and, most importantly, the associated refinement principles. We also experiment with extensions such as event combinators and functional variants of the (relational) refinement principles of Event-B. the embedding is very shallow in that all the constructions are directly based on the Lean4 logic and abstractions, especially dependently-typed structures and type-classes. One benefit is that proof obligations can be discharged using the powerful Lean4 tactic language. Moreover, we enforce the fundamental principle of correctness-by-construction: machine states, events structures and refinement steps cannot be fully constructed without discharging the prescribed proof obligations.

关键词： State-based formal method Refinement Functional domain modeling

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Personalized Trajectory Prediction for Driving Behavior modeling in Ramp-Merging Scenarios 17

Personalized Trajectory Prediction for Driving Behavior Mode...

引用

7th IEEE international conference on Robotic Computing (IRC)

作者： Li, Siyan Wei, Chuheng Wu, Guoyuan Barth, Matthew J. Abdelraouf, Amr Gupta, Rohit Han, Kyungtae Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92507 USA Toyota North Amer InfoTech Labs Toyota CA 94043 USA

ISBN: (纸本)9798350395747

Despite numerous studies on trajectory prediction, existing approaches often fail to adequately capture the multifaceted and individual nature of driving behavior. In recognition of this gap and based on DenseTNT, an end-to-end and goal-based trajectory prediction method, our study developed a new version of DenseTNT that incorporates personalized nodes within the graph neural network in VectorNet as context encoder. throughout the neural network computations, these nodes represent individual driver labels, allowing a more granular understanding of diverse driving behaviors to be gained. Based on comparative analysis, our model has a 11.4% reduction in minADE when compared to baseline models that do not have personalized labels.

关键词： connected and automated vehicles (CAV) trajectory prediction personalized driving behavior ramp merging

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the state of eHealth research across information technologies: A longitudinal analysis using topic modeling to address future scholarship 56

The state of eHealth research across information technologie...

引用

56th Annual Hawaii international conference on System Sciences (HICSS)

作者： Britt, Rebecca K. Chou, Suyu Omah, Ozioma Chakraborty, Ananya Raka Univ Alabama Tuscaloosa AL 35487 USA

ISBN: (纸本)9780998133164

eHealth research has been marked by the last two decades of scholarship spurred by technological advances and the potential of health promotion and behavior change. the study examined the state of eHealth scholarship across social, behavioral and information technologies through a systematic, machine-based learning approach of the last 19 years across 811 articles. the study analyzes topics that were published using latent Dirichlet allocation of studies from 2002 to 2021;it also raises ethical challenges for researchers related to those in prior health initiatives by the CDC and in current scholarship. Results show the common topics, terms, and linguistic attributes within the state of eHealth scholarship and disparities in other areas based on topics published. Suggestions are offered for interdisciplinary collaboration to facilitate the growth and the optimal, practical use of eHealth and directions for the future.

关键词： eHealth and information sciences health technologies topic modeling technology adoption ethical challenges

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Dynamic Performance Assessment through Simulated Model of Photovoltaic Water Pumping System for Off-Grid Communities 12

Dynamic Performance Assessment through Simulated Model of Ph...

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12th international conference on Renewable Energy Research and Applications (ICRERA)

作者： Zafar, Sohaib Khalid, Muhammad Punjab Tianjin Univ Technol Dept Elect Engn Lahore Pakistan King Fahd Univ Petr & Minerals KFUPM Dept Elect Engn Dhahran Saudi Arabia KFUPM Interdisciplinary Res Ctr Renewable Energy & Powe Dhahran Saudi Arabia

ISBN: (纸本)9798350337938

the provision of the clean drinking water in remote and energy constrained areas have been a persistent problem in the developing world. Developing countries in general have financial constraints to expand electric grid, thus impeding the energy servicing for essential *** this end, Solar photovoltaic-powered water pumping systems (SPVWPS) have long been explored as a highly potent solution for provision of water in off-grid areas. In this context, many modeling exercises have been conducted to develop customized and optimal solutions for specific climatic conditions of different regions in the world. Most of these models simulate or validate critical aspects of the systems, such as pumped volumetric flow using climatic data as model input. these models are centered around different aspects of the energy conversion chain and models of solar photovoltaic arrays. It is rare to see models involving the volumetric flow rate by users as an input parameter. this study incorporates volumetric flow rate of the water usage as an input parameter. Such a model has not been developed and simulated for particular climatic conditions of Pakistan. this study used climatic data ( Ambient Temperature and Irradiance) and water usage volumetric flow rate as input *** water usage flow rate helps incorporate user behavior in the model, an under explored research gap in such SPVWPS models. the model was simulated on MATLAB Simulink, using the climatic data for Lahore, Pakistan. the representative reliability of the system design were assessed. the model serves as a simple, yet effective tool to gauge the reliability of SPVWPS designed on a static lump sum water demand basis in a dynamic setting. this reduces the chances of the over or under sizing the system and gives a preliminary estimate of the need and feasibility to incorporate cutting-edge technologies such as AI in such systems.

关键词： Solar Photovoltaics Water Pumping System Renewable Energy modeling Performance Assessment

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